工件的释放时间和加工时间具有一致性的单机在线排序问题研究

工件的释放时间和加工时间具有一致性, 是指释放时间大的工件其加工时间不小于释放时间小的工件的加工时间, 即若$r_{i}\geq r_{j}$, 则$p_{i}\geq p_{j}$。本文在该一致性约束下, 研究最小化最大加权完工时间单机在线排序问题, 和最小化总加权完工时间单机在线排序问题, 并分别设计出$\frac{\sqrt{5}+1}{2}$-竞争的最好可能在线算法。

Face recognition algorithm based on orthogonal and sparse constrained nonnegative tensor factorization

As a feature extraction method, nonnegative tensor factorization has been widely used in image processing and pattern recognition for its advantages of preserving the internal structural features of data and strong interpretability. However, there are two problems in this method: one is that there is unnecessary correlation between the decomposed base images, which leads to more redundant information and takes up a lot of memory; the other is that the coding is not sparse enough, which leads to the expression of the image is not concise enough. These problems will greatly affect the accuracy of face recognition. In order to improve the accuracy of face recognition, a face recognition algorithm based on orthogonal and sparse constrained nonnegative tensor factorization is proposed. Firstly, orthogonal and sparse constraints are added to the traditional nonnegative tensor factorization to reduce the correlation between the base images and obtain sparse coding. Secondly, the original face image and the decomposed base image are used to calculate the low dimensional feature representation of the face. Finally, cosine similarity is used to measure the similarity between low-dimensional features and judge whether two face images represent the same person. Through experiments in AR database and ORL database, it is found that the improved algorithm can achieve better recognition effect.